||Grabner Michael, Grabner Helmut, Bischof Horst
Asian Conference on Computer Vision, Tokyo, Japan
The ability to detect and recognize individuals is essential
for an autonomous robot interacting with humans even if computational
resources are usually rather limited. In general a small user group can
be assumed for interaction. The robot has to distinguish between multiple
users and further on between known and unknown persons. For
solving this problem we propose an approach which integrates detection,
recognition and tracking by formulating all tasks as binary classification
problems. Because of its efficiency it is well suited for robots or other
systems with limited resources but nevertheless demonstrates robustness
and comparable results to state-of-the-art approaches. We use a common
over-complete representation which is shared by the different modules.
By means of the integral data structure an efficient feature computation
is performed enabling the usage of this system for real-time applications
such as for our autonomous robot Flea.